Positive selection, or adaptive evolution, is the driving force of Darwinian evolution and acts to increase the frequency of advantageous alleles in a population. Despite intense interest and study a detailed understanding of the adaptive landscape of the human genome has remained elusive. Identifying regions of the genome that have been targets of adaptive evolution will provide important insights into human evolutionary history and facilitate the identification of complex disease genes. The long-term goal of this project is to further our knowledge of how positive selection has contributed to extant patterns of human genetic variation by identifying genes that have been subject to adaptive evolution. To this end, in specific aim 1, we will use dense catalogs of publicly available SIMP data to perform genome-wide scans for positive selection and identify candidate selection genes. In contrast to the traditional paradigm of studying a small number of loci that one hypothesizes a priori to be influenced by selection, a population genomics approach allows global and unbiased inferences about selection to be made. In specific aim 2, we will confirm the signature of positive selection in 30 candidate selection genes by resequencing them in 88 individuals (22 each from African, Chinese, European, and Japanese populations) and 7 non-human primates. High resolution DNA sequence data will allow detailed evolutionary hypotheses to be tested. Specifically, statistical tests of neutrality based on levels of intraspecific polymorphism and interspecific divergence will be performed, and the magnitude and ages of selective events will be estimated. Finally, in specific aim 3, we will genotype SNPs in confirmed genes subject to adaptive evolution in a geographically diverse panel of 1,064 individuals. These data will provide important insights into the geographic distribution of genetic variation subject to adaptive evolution and allow us to test the hypothesis that selected allele and haplotype frequencies are correlated with environmental attributes such as latitude, altitude, or climate. The data generated in this project will have a significant impact on public health. One of the most difficult challenges confronting human genetics is to find genes that contribute to common complex diseases such as diabetes, cancer, and hypertension. Research that increases our understanding of the evolutionary forces that shape patterns of human genetic variation will facilitate disease gene mapping studies.